Demographic attributes cannot currently be used in predictive models for high-stakes and commercially viable uses of explainable ML in credit lending, insurance, and employment in the U.S. that fall under FCRA, ECOA, or other applicable regulations. Thus their contribution to models cannot be assessed using accurate, direct explainable ML techniques. Even when demographic attributes can be used in models, it has been shown that explanations may not detect unwanted sociological bias [1]}. Given these drawbacks, it is recommended that fairness techniques are used to test for and remediate bias, and explanations are used to understand bias when appropriate (see Corollary REF ).
